Connecting Visual Attention to Choice: Uncovering Nudge Mechanisms Through Eye-Tracking in Virtual Reality

Abstract

This paper investigates how eye-tracking data can help explain why interventions work or don’t. Using an immersive virtual reality experiment in which participants repeatedly chose between taxi and bus, we compare a series of multinomial logit (MNL) and Decision Field Theory (DFT) models under static and dynamic specifications, with and without gaze-based information. The results show that eye-tracking improves behavioural interpretation in two ways. First, it helps distinguish which specific intervention elements entered the decision process, showing that not all parts of a nudge are equally effective. Second, when incorporated into DFT, gaze data provide a direct link between observed attention and preference formation. Across models, the dynamic structure better captures repeated decision making, and DFT offers a modest but consistent improvement in model fit. The findings provide useful guidance for policy makers by showing how intervention design can be improved through attention-based behavioural insights.